# Introductory Statistics with R, 2nd edition

## by Peter Dalgaard

### Paperback 229mm × 155mm, xvi+364 pages Published in August 2008.

NOTE: This page has been updated for the 2nd edition. The old page (1st ed.) has been moved here

### Description:

R is an Open Source implementation of the S language. It works on multiple computing platforms and can be freely downloaded. R is now in widespread use for teaching at many levels as well as for practical data analysis and methodological development.

This book provides an elementary-level introduction to R, targeting both non-statistician scientists in various fields and students of statistics. The main mode of presentation is via code examples with liberal commenting of the code and the output, from the computational as well as the statistical viewpoint. Brief sections introduce the statistical methods before they are used. A supplementary R package can be downloaded and contains the data sets.

The statistical methodology includes statistical standard distributions, one- and two-sample tests with continuous data, regression analysis, one- and two-way analysis of variance, regression analysis, analysis of tabular data, and sample size calculations. In addition, the last six chapters contain introductions to multiple linear regression analysis, linear models in general, logistic regression, survival analysis, Poisson regression, and nonlinear regression.

In the second edition, the text and code have been updated to R version 2.6.2. The last two methodological chapters are new, as is a chapter on advanced data handling. The introductory chapter has been extended and reorganized as two chapters. Exercises have been revised and answers are now provided in an Appendix.

### Contents

1. Basics
2. The R environment
3. Probability and distributions
4. Descriptive statistics and graphics
5. One and two-sample tests
6. Regression and correlation
7. Analysis of variance and the Kruskal-Wallis test
8. Tabular data
9. Power and the computation of sample size
11. Multiple regression
12. Linear models
13. Logistic regression
14. Survival analysis
15. Rates and Poisson regression
16. Nonlinear curve fitting

#### Appendices:

1. Obtaining and Installing R
2. Data sets in the ISwR package
3. Compendium